Sustainability Assessment Decision OptimizationEPA Grant Number: SU839277
Title: Sustainability Assessment Decision Optimization
Investigators: Chen, Victoria C.P.
Institution: The University of Texas at Arlington
EPA Project Officer: Page, Angela
Project Period: August 1, 2017 through July 31, 2018
Project Amount: $15,000
RFA: P3 Awards: A National Student Design Competition for Sustainability Focusing on People, Prosperity and the Planet (2017) RFA Text | Recipients Lists
Research Category: Sustainability , P3 Awards , P3 Challenge Area - Built Environment
Architectural designers and building engineers currently possess software used to analyze various performance objectives for green building. However, existing green building software tools do not optimize decision-making across multiple areas such as energy usage, cost efficiency, and environmental impact. No integrated approach has yet been developed which can combine these areas into a total sustainability assessment.
The objective of our proposed project is to work towards closing the loop on lifecycle of building construction and operations by developing an initial sustainability assessment decision optimization for larger green building framework that employs software tools in an integrated manner.
The proposed project addresses the fundamental objective of solving real world green building based decision-making problems with an integrated engineering, statistics, and optimization approach. The following project tasks are proposed: (1) Calibrate the software tools for selected case studies. Complete a sustainability assessment of the selected case studies with a life cycle analysis comparing building options. (2) Develop a design of experiment process that accommodates uncertainty and a mix of many categorical and continuous variables. (3) Build multi-response treed regression metamodels to represent the relationships between computer model inputs and performance outputs, where the multi-response structure corresponds to multiple performance outputs. (4) Develop and implement a multi-objective global optimization process. (5). Developing a decision support tool which will help the user identify design variables that affect the performance of the building and identify nondominated building designs.
Our proposed research will provide guidance to users in a comprehensive and realistic analysis that dovetails with their specific needs. The result of the green building framework is that the key stakeholders will acquire recommendations on building options subject to their identified performance requirements. Our approach will be applicable for both new construction and retrofitting of existing buildings. The success of this project will advance decision-making for the built environment and lead to larger scale applications, potentially addressing the larger built infrastructure, by enabling comprehensive system decision optimization via computer models. Also, the successful implementation of this project could provide guidance back to the sustainability and green building research communities on key directions for developing technologies.